Plant emergence system

ABSTRACT

An unmanned image capture system captures images of a field or work area using a first, spectral image capture system and a second video image capture system. Crop location data that is indicative of the location of crop plants within the field, is obtained. Evaluation zones in the image data generated by the first image capture system are identified based on the crop location data. Crop plants within the evaluation zones are then identified, analyzed to generate a corresponding emergence metric, and linked to a corresponding video image generated by the second image capture system.

FIELD OF THE DESCRIPTION

The present description relates to plant emergence. More specifically,the present description relates to plant emergence analysis using imagescollected from an unmanned system.

BACKGROUND

There are a wide variety of different types of farming techniques. Onesuch technique is referred to as precision farming. Precision farming,or precision agriculture, is also referred to as site-specific cropmanagement. The technique uses observation and measurement of variationsof different criteria at specific sites, from field-to-field, and evenwithin a single field. The observation and measurement of the variationin the different criteria can then be acted on in different ways.

The effectiveness of precision farming depends, at least in part, uponthe timely gathering of information at a site-specific level, so thatinformation can be used to make better decisions in treating andmanaging the crop. This type of information can include information thatis indicative of plant emergence characteristics (such as maturity,emergence uniformity, etc.) pest presence, disease, water and nutrientlevels, weed stresses, etc.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

An unmanned image capture system captures images of a field or work areausing a first spectral image capture system, and a second video imagecapture system. Crop location data that is indicative of the location ofcrop plants within the field, is obtained from the planting machines orother historical sources. Evaluation zones in the image data generatedby the first image capture system are identified based on the croplocation data. Crop plants within the evaluation zones are thenidentified, analyzed to generate a corresponding plant emergence metric,and linked to a corresponding video image generated by the second imagecapture system.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to implementationsthat solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one example of a plant analysisarchitecture.

FIG. 2 is one example of a more detailed block diagram of a plantevaluation system.

FIG. 3 is a flow diagram illustrating one example of the overalloperation of the architecture shown in FIG. 1 in generating a pluralityof different crop images.

FIG. 4 is a flow diagram illustrating one example of the operation ofthe plant analysis system in applying spatial and spectral filtering toobtain a crop image.

FIGS. 4A-4C show examples of user interface displays that can begenerated by the plant evaluation system.

FIG. 5 is a flow diagram illustrating one example of the operation of aruntime system.

FIGS. 5A-5C show examples of user interface displays that can begenerated by the plant evaluation system.

FIG. 6 is a block diagram of one example of the architecture illustratedin FIG. 1, deployed in a remote server architecture.

FIGS. 7-9 are examples of mobile devices that can be used in thearchitectures illustrated in the previous figures.

FIG. 10 is a block diagram of one example of a computing environmentthat can be used in the architectures shown in the previous figures.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of one example of a plant analysisarchitecture 100. In the example shown in FIG. 1, architecture 100illustratively includes unmanned image capture system 102, plantingmachine 104, plant evaluation system 106, and it can include remotesystems 108 and other machines 110. Plant evaluation system 106 is showngenerating user interfaces 116 with user input mechanisms 118, foraccess by user 114. User 114 can illustratively interact with user inputmechanisms 116 in order to control and manipulate plant evaluationsystem 106.

Also, in the example shown in FIG. 1, the items are illustrativelyconnected to one another over a network 112. Network 112 can be any of awide variety of different types of networks, such as the Internet oranother wide area network, a variety of other wireless or wirednetworks, etc.

Before describing the items in architecture 100 in more detail, a briefoverview of the operation of architecture 100 will first be provided. Inone example, unmanned image capture system 102 includes an unmannedaerial vehicle, although other unmanned systems could be used as well.System 102 illustratively captures spectral images of a field underanalysis, as well as video images. Geographic location information isassociated with those images, and they are provided to plant evaluationsystem 106. Planting machine 104, when it planted the crops in the fieldunder analysis, illustratively generated geographic location informationthat identifies the location of the plants in the field, or the rows ofcrops in the field using, for instance, RTK GPS system. This informationis also provided to plant evaluation system 106. Plant evaluation system106, itself, illustratively identifies evaluation zones in the fieldunder analysis based on the location of the crops in that field. It thenanalyzes the spectral images received from image capture system 102 toidentify crop plants in the evaluation zones. It evaluates the images ofthose crop plants to determine an emergence characteristic correspondingto those plants. For instance, it may determine whether the plants areemerging uniformly, ahead of schedule, behind schedule, or not emerging,among other things. It divides the images of the field into gridsections and assigns an emergence summary metric, which is indicative ofthe emergence characteristics of plants in that grid, to each gridsection. It then links the video images of each grid section, alsoreceived from image capture system 102, to the grid sections. Then, whena user 114 accesses plant evaluation system 106, the user canillustratively view an image of the grid sections that shows the variousemergence summary metrics. Also, the user can easily navigate to thevideo image so the user can also see a video image of any particulargrid section.

A more detailed description of each of the items in architecture 100will now be provided. System 102 illustratively includes spectralimaging system 120, video imaging system 122, geographic location system124, communication system 126, data store 128, processor 130, and it caninclude a wide variety of other items 132, as well. Spectral imagingsystem 120 illustratively includes a camera that takes spectral imagesof the field under analysis. For instance, the camera can be amultispectral camera or a hyperspectral camera, or a wide variety ofother devices for capturing spectral images. Video imaging system 122illustratively includes a camera that captures images in the visible orthermal infrared range. For example, it can be a visible light videocamera with a wide angle lens, or a wide variety of other video imagingsystems.

Geographic location system 124 can include location determining logic140, correction logic 142, and it can include other items 144. Thelocation determining logic 140 can, for instance, be a satellitenavigation receiver that receives satellite information from apositioning satellite. Correction logic 142 can include a correctionreceiver or transceiver which can, for example, receive correctioninformation from a differential correction base station, or from asatellite, or a real time kinematic information that is used to corrector enhance the precision of, position data received from a globalnavigation satellite system. In one example, the geographic locationinformation generated by system 124 can have a spatial resolution on theorder of several centimeters, once corrected, although less precisesystems can just as easily be used. These are examples only, and someother examples of geographic location systems are discussed below.

Communication system 126 illustratively includes wireless communicationlogic 146, store and forward management logic 148, and it can includeother items 150. Wireless communication logic 146 can be substantiallyany wireless communication system that can be used by image capturesystem 102 to communicate information to the other items in architecture100. Store and forward management logic 148 illustratively storesinformation, when transmission is not possible for whatever reason, andthen forwards it once transmission becomes possible again. Some examplesof store and forward scenarios are described in further detail below.

Data store 128 illustratively stores the spectral images 152 generatedby spectral imaging system 120. It also illustratively stores the videoimages 154 generated by video imaging system 122. As briefly mentionedabove, when the images are taken by imaging systems 120 and 122,geographic location system 124 illustratively generates geographiclocation information corresponding to a geographic location reflected inthose images. Thus, data store 128 illustratively stores thecorresponding geographic location information 156 for the spectralimages 152 and video images 154 and it can store other items 157 aswell.

Planting machine 104 illustratively includes a set of plantingmechanisms 160, a geographic location system 162, a communication system164, and it can include a wide variety of other planting machinefunctionality 166. As planting machine 104 travels over the field, itplants crop, illustratively in rows, using planting mechanisms 160.Geographic location system 162 can be the same type as geographiclocation system 124 described above with respect to image capture system102, or a different type of system. It illustratively generatesgeographic location information that identifies the geographic locationof the crop plants (or at least the rows of crop plants) when they areplanted in the field. Communication system 164 can be the same type ofsystem as system 126, or a different type of system. It illustrativelycommunicates the geographic location information identifying thelocation of the crop plants, or rows, over network 112.

Other machines 110 can include a wide variety of different machines. Forexample, they can include other planting machines or even other machinesthat travel over the field to apply pesticides, herbicides, fertilizer,etc.

Remote systems 108 can also be a wide variety of different systems. Theycan be remote server environments, remote computer systems that may beused, for instance, by a farmer, a farm manager, etc. They can also beremote computing systems, such as mobile devices, remote networks, or awide variety of other remote systems. In one example, the remote systems108 include one or more processors or servers 170, data store 172, andthey can include other items 174. The remote systems 108 can beconfigured as a remote server environment that stores the image data 176generated by image capture system 102 (which can include the spectralimages 152, video images 154, and corresponding location information156) as well as planting location data 178, that is generated byplanting machine 104, which indicates the location of crop plants orplant rows in the field. This is just one example of a remote system108, and a wide variety of others can be used as well.

Plant evaluation system 106 illustratively includes one or moreprocessors or servers 180, a user interface component 182, data store184, image analysis system 186, runtime system 188, communicationcomponent 189, and it can include a wide variety of other items 190. Inone example, plant evaluation system 106 can run one or moreapplications that comprise image analysis system 186 and runtime system188. Systems 186 and 188 can be embodied as other items as well. Imageanalysis system 186 illustratively receives the image data (eitherdirectly from image capture system 102, or from remote system 108, orfrom another location), along with the plant location data 178 (eitherfrom planting machine 104, remote system 108, or another system). Itthen evaluates the image data to determine any of a number of differentcrop characteristics. For instance, it can determine whether the crop isemerging uniformly, at a rate that is ahead of schedule, behindschedule, or that it is not emerging, etc. This analysis is performed byfirst identifying evaluation zones based upon the location of the cropplants (or rows) in the field, and then performing spectral analysiswithin those evaluation zones to identify the crops, and variouscharacteristics of those crops.

Runtime system 188 can be used by user 114 to access that information.For instance, the user can control runtime system 188 to display thevarious images, emergence summary metrics, or other analysis resultsgenerated by image analysis system 186. In addition, the user can viewvideo images taken by video imaging system 122 to visually verify, orotherwise evaluate, the veracity of the analysis results. In addition,user 114 can evaluate how to treat the field (or various sites withinthe field) based upon the analysis results. In one example, imageanalysis system 186 also generates recommendations for treating variousspots within the field, based upon the analysis data. This can varywidely from things such as applying more fertilizer, applying nofertilizer, replanting, etc. Communication component 189 can be used tocommunicate the analysis results, or other information generated byplant evaluation system 106, to other items in architecture 100 as well.

FIG. 2 shows one example of a more detailed block diagram of plantevaluation system 106. FIG. 2 shows that, in one example, image analysissystem 186 illustratively includes mosaic generation logic 200,geo-referencing logic 202, spatial filtering logic 204, and spectralfiltering logic 206. It can also include grid generation logic 208,emergence summary metric generation logic 210, video link generationlogic 212, and it can include other items 214. Also, in the exampleshown in FIG. 2, runtime system 188 illustratively includes navigationlogic 216, display generation logic 218, user interaction detectionlogic 220, recommendation logic 222, and it can include a wide varietyof other runtime functionality 224.

FIG. 3 is flow diagram illustrating one example of the operation ofimage analysis system 186 in generating a variety of different imagesfor access by user 114. Unmanned image capture system 102 first collectsimage data for the field under analysis. This is indicated by block 250in FIG. 3. As discussed above, this can include multispectral orhyperspectral images of the field generated by spectral imaging system120. This is indicated by block 252 in FIG. 3. It can also includeclose-up video images of the plants in the field, as generated by videoimaging system 122. This is indicated by block 254 in the flow diagramof FIG. 3. The image data can include a wide variety of other image dataas well, and this is indicated by block 256.

Geographic location system 124 then obtains geographic locationinformation corresponding to each of the images. This is indicated byblock 258. Again, as discussed above, it can include GPS coordinatesreceived by a GPS receiver and corrected by correction information usingcorrection logic 142. It can, of course, include a wide variety of otherinformation as well.

Mosaic generation logic 200 in image analysis system 186 of plantevaluation system 106 then mosaics the images and geo-references themrelative to ground control points. This is indicated by block 260 in theflow diagram of FIG. 3. In order to mosaic the images, the geographiclocation information corresponding to each of the images is used tostitch them together into a larger image of the field under analysis.The geo-referencing logic 202 can automatically geo-reference the imageagainst the ground control points, although this can be done manually aswell. This can be done to adjust image alignment or ensure that themosaicked images are substantially seamlessly stitched together with oneanother so that no geographic locations are duplicated or skipped in themosaicked image.

The elements in the mosaicked image are then classified to identify thecrop plants and to extract unwanted plants (such as weeds) from theimage in order to obtain a crop image. The crop image is illustrativelyan image that substantially only contains crop plants with weeds beingremoved. This can be done using both spatial and spectral filtering onthe mosaicked image. One example of this is described in greater detailbelow with respect to FIG. 4. Classifying the plants and extracting theunwanted plants to obtain the crop image is indicated by block 262 inthe flow diagram of FIG. 3.

Grid generation logic 208 then divides the crop image into a grid withmultiple grid sections. This is indicated by block 264. The gridsections can have any suitable dimensions. In one example, the gridsections may be 5 meters by 5 meters square. This is only one exampleand a wide variety of other sizes can be used as well.

For each grid section, emergence summary metric generation logic 210generates a corresponding emergence summary metric that is indicative ofan emergence characteristic of the crop plants, in that grid section.This obtains a crop density summary image that shows the variousemergence summary metrics for the different grid sections for the image.This is indicated by block 266. This can be done in a variety ofdifferent ways. For instance, spectral analysis can be performed on theimage within each grid section to identify a number of pixels in theimage that correspond to crop plants. This can be used to identify acrop plant density within each grid section. This is indicated by block268. The density can then be compared to expected density data thatindicates an expected crop plant density within a grid section of thatsize, in that field. This is indicated by block 270. The comparison cangenerate a result that indicates whether the actual crop density is thesame, above, or below the expected crop density value. This metric canindicate whether the emergence rate of the plants in that grid sectionis above, roughly equal to, or below the expected emergence rate.

In another example, the crop density value can be compared to one ormore different density threshold values. This is indicated by block 272.The result of that comparison can be used to generate the emergencesummary metric for that grid section. The various grid sections can thenbe color-coded based upon the emergence summary metric corresponding toeach of the grid sections. This is indicated by block 274. For instance,a grid section that has an emergence metric that is above an expectedvalue may have one color, while a grid section which has an emergencemetric that is approximately equal to the expected value may have asecond color, and a grid section that has an emergence metric below theexpected value may have a third color. In addition, those grid sectionswhere substantially no crop plants have emerged may have yet a differentcolor. Further, in the example where the crop density metric is comparedagainst various different threshold values, the grid section can becolor-coded based upon where the observed crop density falls relative tothe threshold values. Also, the grid sections need not be color-coded,but may have a different visual indicator applied to them indicating thecrop density metric. For instance, those that have a crop density thatis approximately equal to an expected crop density may simply bestatically displayed. However, those that have a crop density below theexpected level may be dynamic (such as flashing at a first rate) whilethose that have a crop density higher than the expected value may bedynamic in a different way (such as flashing at a second rate).Similarly, other visual indicia may be used. For example, some gridsections may be bolded or outlined in a heavy outline, or otherwisevisually distinguished from the other grid sections. These are examplesonly, and a wide variety of other visual indicia can be used.

Also, in one example, each grid section has an alpha-numeric metricvalue generated therefore. This is indicated by block 276. For instance,if the grid section has a crop density that exceeds the expected cropdensity, an alpha-numeric display of “>100%” may be displayed on thatgrid section. If it has a crop density that is below the expected value,then an alpha-numeric value of “<100%” may be displayed on that gridsection, etc. Again, these are only examples of different alpha-numericmetric values that can be generated for, and displayed on, each gridsection. A wide variety of other emergence summary metrics can begenerated for the grid sections, and this is indicated by block 278.

Video link generation logic 212 then generates a link, for each gridsection. The link links a corresponding grid section with the videoimage for that grid section generated by video imaging system 122. Thisis indicated by block 280. For instance, video link generation logic 212can access the geographic location information corresponding to eachindividual grid section, and can find the video image that has the samecorresponding geographic location information. The link generation logic212 can then generate an actuatable link from the grid section, in agrid section display, to the video. Therefore, when the user actuates agrid section, in the grid section display, this can activate the link sothat the corresponding video image can be displayed to the user. Thisallows the user to evaluate the veracity of the emergence summary metricand to get an actual view of the crop plants in order to perform thatevaluation.

Communication component 189 can then output the mosaicked,geo-referenced images, the crop image (that shows the crop plants) andthe crop density summary image (that shows the grid sections and thecorresponding emergence summary metrics) along with the links to thecorresponding summary images, to other systems. This is indicated byblock 282. In one example, it can be output to a remote system 108 forstorage in a corresponding data store 172. This is indicated by block284. It can be output using a user interface 116, for user interactionby a user 114. This is indicated by block 286. It can be output forother analysis or for further processing at a wide variety of othersystems or machines as well. This is indicated by block 288. Forinstance, it can be output to a machine that is applying fertilizer toindicate whether, at each grid section, the machine should applyfertilizer, whether it should apply extra fertilizer, whether it shouldnot apply any fertilizer, etc. The information can be used to generatecrop or field treatment recommendations or it can be output in a varietyof other ways as well, and this is indicated by block 290.

FIG. 4 is a flow diagram illustrating one example of how the mosaicked,geo-referenced image is classified to generate the crop image. Recallthat the crop image shows substantially only crop plants so that thecrop density, or other emergence characteristic, can be obtained. FIGS.4A-4C show various images. FIGS. 4-4C will now be described inconjunction with one another.

Mosaic generation logic 200 and geo-referencing logic 202 are first usedto generate the mosaicked, geo-referenced image data for the field underanalysis. This is indicated by block 292 in FIG. 4. FIG. 4A shows oneexample of a mosaicked, geo-referenced, spectral image 294 for a givenfield of interest. It can be seen that the crop rows are roughlydistinguishable in the spectral image 294.

Spatial filtering logic 204 then obtains the previously-generated croplocation data which provides a geographic location of the rows of cropplants (or the plants themselves). This is indicated by block 296. FIG.4B shows one example of a display showing the geographic location of thecrops based on the previously-generated crop location data. As discussedabove, this can be previously-generated information, that is generatedusing a planting machine 104. This is indicated by block 298 in the flowdiagram of FIG. 4. Of course, the crop location data can be obtainedfrom other sources as well, and this is indicated by block 300.

Spatial filtering logic 204 then applies spatial filtering to thespectral image using the crop location data in order to identifyevaluation zones that will be used to evaluate a crop emergencecharacteristic (such as crop density). Applying the spatial filtering isindicated by block 302. In one example, the crop location dataidentifies the geographic location of the crop rows. Therefore, spatialfiltering logic 204 identifies a reference line corresponding to thecenter of each crop row, and also identifies a margin window around thatreference line, for each row. The margin window identifies an evaluationzone for a given row. It is assumed that anything growing between twoadjacent evaluation zones (for two adjacent crop rows) is a plant thatis growing between the rows and therefore is very likely a weed or someother undesirable plant. Thus, the evaluation zones identify a zone forspectral filtering for each crop row. The spectral filtering need not beperformed on any portion of the images, other than within the evaluationzones. Identifying an evaluation zone as a crop row location plus amargin window around the crop row location is indicated by block 304 inFIG. 4. The spatial filtering can be applied in other ways to identifyevaluation zones, and this is indicated by block 306.

Spectral filtering logic 206 then applies spectral filtering in theevaluation zones to evaluate crop plants within those evaluation zones,and to generate the crop image, which substantially shows only cropplants in the spectral image. This is indicated by block 308. In oneexample, spectral filtering logic 206 identifies pixels, within theevaluation zones defined in the spectral image, that have a spectralsignature that corresponds to the crop plants. The crop plants will havea spectral signature that differs from that of soil, and from that of avariety of different types of weeds. Therefore, by filtering the data inthe evaluation zones in the spectral image to only show plants with aspectral signature corresponding to a crop plant, the crop image can beobtained. Identifying crop plants based on spectral signature isindicated by block 310. Identifying and removing weeds based upon theirspectral signature is indicated by block 312. The crop image can begenerated in other ways as well, and this indicated by block 314.

FIG. 4C shows one example of a crop image 316. It can be seen that image316 differs from image 294 (shown in FIG. 4A). Image 316 more clearlyshows only crop plants in rows, and the spectral informationcorresponding to weeds or other plants has been filtered out based onthe crop location data (e.g., as shown in FIG. 4B). The crop image canthen be output for further processing (such as to identify grid sectionswith various crop density metrics, etc.). This is indicated by block 318in FIG. 4.

FIG. 5 is a flow diagram illustrating one example of the operation ofruntime system 188 in plant evaluation system 106. FIGS. 5A-5C showvarious user interface displays that can be generated during runtime,although other or different displays can be generated as well. FIGS.5-5C will now be described in conjunction with one another.

Runtime system 188 first receives a user input from user 114 indicatingthat the user wishes to access the plant evaluation system 106. This isindicated by block 350 in FIG. 5. For instance, the user can inputauthentication information 352 or provide the user input in other ways354. Display generation logic 218 then generates a field selectiondisplay with a set of user input mechanisms. In one example, the usercan actuate one of the user input mechanisms to select a field for whichinformation is to be accessed. Generating the field selection userinterface display is indicated by block 356.

User interaction detection logic 220 then detects a user input selectinga field. This is indicated by block 358. Display generation logic 218then generates a field display indicative of the selected field. This isindicated by block 360. The field display can take a wide variety ofdifferent forms. In one example, it is the mosaicked image of theselected field, along with user actuatable filter input mechanisms thatallow the user to filter that input to see a variety of differentinformation corresponding to the selected field. This is indicated byblock 362. Of course, it can be any other display for the field as well,and this is indicated by block 364.

User interaction detection logic 220 then detects user actuation of afilter input mechanism to see emergence information for the selectedfield. This is indicated by block 366.

In response, display generation logic 218 can access any of the variousdisplays or images that were generated by image analysis system 186,that are indicative of plant emergence in the selected field. That imagecan be referred to as the emergence display. This is indicated by block368. For instance, it can be the mosaicked, geo-referenced images, asindicated by block 370. It can be the crop image (such as that shown inFIG. 4B) as indicated by block 372. It can be a crop density summaryimage with links to the underlying video images. This is indicated byblock 374. It can also be other images as well, as indicated by block376.

FIG. 5A shows one example of a crop density summary image 378. It can beseen that the image 378 has grid sections defined by the squares on theimage. The grid sections can each have one of a plurality of differentcolors based upon their corresponding emergence summary metric. Forinstance, a first color (e.g., green) grid section can indicate that thecrop density is at an expected level in that grid section. A secondcolor (e.g., red) grid section may indicate that the crop density levelis either zero or below a given threshold, and a third color gridsection can indicate that the crop density is somewhere between anexpected level and the lower threshold. A different color may indicatethat the crop density in the corresponding grid section is above anexpected level, or that it is at some other level. FIG. 5B shows asecond example of a crop density summary image.

FIG. 5B shows that the underlying crop image has been removed andreplaced by the grid sections. Thus, the grid sections shown in FIG. 5Balso have alphanumeric metrics displayed on them. The metrics areillustratively crop density metrics indicative of the crop density inthe corresponding grid sections. Again, the grid sections can also becolor-coded to indicate where the crop density falls relative toexpected data or various crop density thresholds.

Once the emergence display is displayed (such as the crop densitydisplays shown in FIGS. 5A and 5B) the user can, in one example,interact with those images. For instance, each of the grid sections maybe an actuatable link. In another example, the displayed image isassociated with a time lapse mechanism that the user can actuate to showcorresponding images that were generated earlier or later in time fromthe currently displayed images. All of these and other examples of userinput mechanisms can be used.

For instance, in one example, user interaction detection logic 220detects a user interaction with the displayed emergence display. This isindicated by block 390 in the flow diagram of FIG. 5. Runtime system 188then performs an action based upon the detected user interaction. Thisis indicated by block 392. The actions can take a wide variety ofdifferent forms. For instance, in one example, navigation logic 216navigates among images that were taken at a particular time. The imagesmay be different portions of the field under analysis, or they may befor different fields. Navigating among different images is indicated byblock 394. In another example, the action may be to display time lapsedimages (or to display them side-by-side with one another) based upon theuser interaction. This is indicated by block 396. For instance, if auser is viewing the crop emergence display, but a plurality of differentcrop emergence displays have been generated over time, the user mayactuate a user input mechanism (such as by moving a slider on thedisplay) to advance either forward or backward in time between thenewest, and oldest, emergence density images. The time lapsed images canbe displayed in other ways as well.

Further, each grid section may be an actuatable link that, when actuatedby the user, navigates the user to the underlying video image for thatcorresponding grid section. Actuating the link to see the ground statusvideo for a selected grid section is indicated by block 398.

FIG. 5C shows one example of a video image. In the example shown in FIG.5C, image 400 illustratively corresponds to a grid section on thedisplay of either FIG. 5A or 5B. When the user actuates the gridsection, the user can see the corresponding video image to verify theveracity of the emergence metric that is displayed in that grid section.

The actions that are performed based on detected user interactions cantake a wide variety of other forms as well. This is indicated by block402.

It can thus be seen that the present system provides a number ofadvantages. Applying spatial filtering based on plant row locations (orindividual plant locations) increases the speed and efficiency in theprocessing. Evaluation zones can be identified around the plant rowlocations so that spectral filtering only needs to be performed withinthose zones instead of for the entire image. Generating an emergencemetric, and color-coding or otherwise providing visual indiciaindicative of where that metric falls with respect to an expected value,also surfaces relevant information much more quickly. This can reducethe network bandwidth requirement and other processing loads on thesystem. In addition, by downloading these metrics to other agriculturalmachines, site-specific treatment can be performed quickly, easily, andefficiently. Actions can be taken based upon the plant emergence metricto save chemicals, to save time, and to otherwise improve thesite-specific farming.

It will be noted that the above discussion has described a variety ofdifferent systems, components and/or logic. It will be appreciated thatsuch systems, components and/or logic can be comprised of hardware items(such as processors and associated memory, or other processingcomponents, some of which are described below) that perform thefunctions associated with those systems, components and/or logic. Inaddition, the systems, components and/or logic can be comprised ofsoftware that is loaded into a memory and is subsequently executed by aprocessor or server, or other computing component, as described below.The systems, components and/or logic can also be comprised of differentcombinations of hardware, software, firmware, etc., some examples ofwhich are described below. These are only some examples of differentstructures that can be used to form the systems, components and/or logicdescribed above. Other structures can be used as well.

The present discussion has mentioned processors and servers. In oneexample, the processors and servers include computer processors withassociated memory and timing circuitry, not separately shown. They arefunctional parts of the systems or devices to which they belong and areactivated by, and facilitate the functionality of the other componentsor items in those systems.

Also, a number of user interface displays have been discussed. They cantake a wide variety of different forms and can have a wide variety ofdifferent user actuatable input mechanisms disposed thereon. Forinstance, the user actuatable input mechanisms can be text boxes, checkboxes, icons, links, drop-down menus, search boxes, etc. They can alsobe actuated in a wide variety of different ways. For instance, they canbe actuated using a point and click device (such as a track ball ormouse). They can be actuated using hardware buttons, switches, ajoystick or keyboard, thumb switches or thumb pads, etc. They can alsobe actuated using a virtual keyboard or other virtual actuators. Inaddition, where the screen on which they are displayed is a touchsensitive screen, they can be actuated using touch gestures. Also, wherethe device that displays them has speech recognition components, theycan be actuated using speech commands.

A number of data stores have also been discussed. It will be noted theycan each be broken into multiple data stores. All can be local to thesystems accessing them, all can be remote, or some can be local whileothers are remote. All of these configurations are contemplated herein.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used so thefunctionality is performed by fewer components. Also, more blocks can beused with the functionality distributed among more components.

FIG. 6 is a block diagram of architecture 100, shown in FIG. 1, exceptthat it is deployed in a remote server architecture 500. In an example,remote server architecture 500 can provide computation, software, dataaccess, and storage services that do not require end-user knowledge ofthe physical location or configuration of the system that delivers theservices. In various embodiments, remote servers can deliver theservices over a wide area network, such as the internet, usingappropriate protocols. For instance, remote servers can deliverapplications over a wide area network and they can be accessed through aweb browser or any other computing component. Software or componentsshown in FIG. 1 as well as the corresponding data, can be stored onservers at a remote location. The computing resources in a remote serverenvironment can be consolidated at a remote data center location or theycan be dispersed. Remote server infrastructures can deliver servicesthrough shared data centers, even though they appear as a single pointof access for the user. Thus, the components and functions describedherein can be provided from a remote server at a remote location using aremote server architecture. Alternatively, they can be provided from aconventional server, or they can be installed on client devicesdirectly, or in other ways.

In the example shown in FIG. 6, some items are similar to those shown inFIG. 1 and they are similarly numbered. FIG. 6 specifically shows thatremote systems 108 and plant evaluation system 106 can be located at aremote server location 502. The information can be provided to remoteserver location 502 by unencumbered image capture system 102 andplanting machine 104 in any of a wide variety of different ways.Therefore, user 114 and machines 110 can access those systems throughremote server location 502. This can be done using a user device 506,for instance.

FIG. 6 also depicts another embodiment of a remote server architecture.FIG. 6 shows that it is also contemplated that some elements of FIG. 1are disposed at remote server location 502 while others are not. By wayof example, data store 172 or plant evaluation system 106 can bedisposed at a location separate from location 502, and accessed throughthe remote server at location 502. Regardless of where they are located,they can be accessed directly by user device 506, through a network(either a wide area network or a local area network), they can be hostedat a remote site by a service, or they can be provided as a service, oraccessed by a connection service that resides in a remote location.Also, the data can be stored in substantially any location andintermittently accessed by, or forwarded to, interested parties. Forinstance, physical carriers can be used instead of, or in addition to,electromagnetic wave carriers. In such an embodiment, where cellcoverage is poor or nonexistent, another mobile machine (such as a fueltruck) can have an automated information collection system. As theharvester comes close to the fuel truck for fueling, the systemautomatically collects the information from the harvester using any typeof ad-hoc wireless connection. The collected information can then beforwarded to the main network as the fuel truck reaches a location wherethere is cellular coverage (or other wireless coverage). For instance,the fuel truck may enter a covered location when traveling to fuel othermachines or when at a main fuel storage location. All of thesearchitectures are contemplated herein. Further, the information can bestored on the harvester until the harvester enters a covered location.The harvester, itself, can then send the information to the mainnetwork.

It will also be noted that the elements of FIG. 1, or portions of them,can be disposed on a wide variety of different devices. Some of thosedevices include servers, desktop computers, laptop computers, tabletcomputers, or other mobile devices, such as palm top computers, cellphones, smart phones, multimedia players, personal digital assistants,etc.

FIG. 7 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user's orclient's hand held device 16, in which the present system (or parts ofit) can be deployed. For instance, a mobile device can be deployed inthe operator compartment of machines 104 and 110, or as user device 506for use in generating, processing, or displaying the plant evaluationinformation. FIGS. 8-9 are examples of handheld or mobile devices.

FIG. 7 provides a general block diagram of the components of a clientdevice 16 that can run some components shown in FIG. 1, that interactswith them, or both. In the device 16, a communications link 13 isprovided that allows the handheld device to communicate with othercomputing devices and under some embodiments provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody any processor or server from previous Figures) along a bus 19that is also connected to memory 21 and input/output (I/O) components23, as well as clock 25 and location system 27.

I/O components 23, in one embodiment, are provided to facilitate inputand output operations. I/O components 23 for various embodiments of thedevice 16 can include input components such as buttons, touch sensors,optical sensors, microphones, touch screens, proximity sensors,accelerometers, orientation sensors and output components such as adisplay device, a speaker, and or a printer port. Other I/O components23 can be used as well.

Clock 25 illustratively comprises a real time clock component thatoutputs a time and date. It can also, illustratively, provide timingfunctions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographical location of device 16. This can include, forinstance, a global positioning system (GPS) receiver, a LORAN system, adead reckoning system, a cellular triangulation system, or otherpositioning system. It can also include, for example, mapping softwareor navigation software that generates desired maps, navigation routesand other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. It can also include computer storagemedia (described below). Memory 21 stores computer readable instructionsthat, when executed by processor 17, cause the processor to performcomputer-implemented steps or functions according to the instructions.Processor 17 can be activated by other components to facilitate theirfunctionality as well.

FIG. 8 shows one embodiment in which device 16 is a tablet computer 600.In FIG. 8, computer 600 is shown with user interface display screen 602.Screen 602 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. It can also use an on-screenvirtual keyboard. Of course, it might also be attached to a keyboard orother user input device through a suitable attachment mechanism, such asa wireless link or USB port, for instance. Computer 600 can alsoillustratively receive voice inputs as well.

FIG. 9 shows that the device can be a smart phone 71. Smart phone 71 hasa touch sensitive display 73 that displays icons or tiles or other userinput mechanisms 75. Mechanisms 75 can be used by a user to runapplications, make calls, perform data transfer operations, etc. Ingeneral, smart phone 71 is built on a mobile operating system and offersmore advanced computing capability and connectivity than a featurephone.

Note that other forms of the devices 16 are possible.

FIG. 10 is one example of a computing environment in which elements ofFIG. 1, or parts of it, (for example) can be deployed. With reference toFIG. 10, an example system for implementing some embodiments includes ageneral-purpose computing device in the form of a computer 810.Components of computer 810 may include, but are not limited to, aprocessing unit 820 (which can comprise processors or servers from anyprevious Figure), a system memory 830, and a system bus 821 that couplesvarious system components including the system memory to the processingunit 820. The system bus 821 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Memoryand programs described with respect to FIG. 1 can be deployed incorresponding portions of FIG. 10.

Computer 810 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 810 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. It includeshardware storage media including both volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by computer 810. Communication media may embody computerreadable instructions, data structures, program modules or other data ina transport mechanism and includes any information delivery media. Theterm “modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 831and random access memory (RAM) 832. A basic input/output system 833(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 810, such as during start-up, istypically stored in ROM 831. RAM 832 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 820. By way of example, and notlimitation, FIG. 10 illustrates operating system 834, applicationprograms 835, other program modules 836, and program data 837.

The computer 810 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 10 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, nonvolatile magnetic disk852, an optical disk drive 855, and nonvolatile optical disk 856. Thehard disk drive 841 is typically connected to the system bus 821 througha non-removable memory interface such as interface 840, and optical diskdrive 855 are typically connected to the system bus 821 by a removablememory interface, such as interface 850.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASICs),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 10, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 10, for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 846, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 836, and programdata 837.

A user may enter commands and information into the computer 810 throughinput devices such as a keyboard 862, a microphone 863, and a pointingdevice 861, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 820 through a user input interface 860 that is coupledto the system bus, but may be connected by other interface and busstructures. A visual display 891 or other type of display device is alsoconnected to the system bus 821 via an interface, such as a videointerface 890. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 897 and printer 896,which may be connected through an output peripheral interface 895.

The computer 810 is operated in a networked environment using logicalconnections (such as a local area network—LAN, or wide area network WAN)to one or more remote computers, such as a remote computer 880.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. In a networked environment, program modulesmay be stored in a remote memory storage device. FIG. 10 illustrates,for example, that remote application programs 885 can reside on remotecomputer 880.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

Example 1 is a computing system, comprising:spatial filtering logic that receives image data indicative of an imageof a field and crop location data indicative of a geographic location ofcrops in the field and identifies a crop evaluation zone, on the image,based on the crop location data; and emergence metric generation logicthat generates an emergence metric indicative of an emergencecharacteristic of the crops in the crop evaluation zone in the field,based on spectral image data corresponding to the crop evaluation zonein the image.Example 2 is the computing system of any or all previous exampleswherein the crop location data identifies a geographic location of wherecrop rows were planted in the field and wherein the spatial filteringlogic identifies a row level crop evaluation zone for each crop row inthe field, each row level crop evaluation zone defining a window aroundthe geographic location of where a corresponding crop row was planted inthe field.Example 3 is the computing system of any or all previous examples andfurther comprising: spectral filtering logic that filters spectralinformation in the row level crop evaluation zones to identify a cropdensity in the row level crop evaluation zones.Example 4 is the computing system of any or all previous exampleswherein the spectral filtering logic confines the filtering of thespectral information to the spectral information in the crop evaluationzones.Example 5 is the computing system of any or all previous examples andfurther comprising:grid generation logic that divides the image of the field into a gridwith grid sections, each grid section corresponding to a geographic areain the field.Example 6 is the computing system of any or all previous exampleswherein the emergence metric generation logic generates an emergencemetric, indicative of an emergence characteristic, corresponding to eachgrid section in the grid.Example 7 is the computing system of any or all previous examples andfurther comprising:a runtime system that detects a user request input and generates a cropdisplay that visually displays the grid sections and visual indiciaindicative of the corresponding emergence metrics.Example 8 is the computing system of any or all previous exampleswherein the runtime system displays color coded grid sections as thevisual indicia corresponding to the emergence metric for each gridsection.Example 9 is the computing system of any or all previous exampleswherein the runtime system displays alphanumeric information on each ofthe grid sections as the visual indicia corresponding to the emergencemetric for each grid section.Example 10 is the computing system of any or all previous examples andfurther comprising:video link generation logic that generates a user actuatable link, foreach given grid section, the user actuatable link linking to a videoimage of the field, corresponding to the given grid section.Example 11 is a method, comprising:receiving a field image of a field;receiving crop location data indicative of a geographic location ofcrops in the field;identifying a crop evaluation zone, on the image, based on the croplocation data;performing spectral filtering on the crop evaluation zone to identifycrops in the crop evaluation zone; andgenerating an emergence metric indicative of an emergence characteristicof the identified crops in the crop evaluation zone; andgenerating a user interface indicative of the emergence metric visuallycorresponding to the crop evaluation zone.Example 12 is the method of any or all previous examples wherein thecrop location data identifies a geographic location of where crop rowswere planted in the field and wherein identifying a crop evaluation zonecomprises:identifying a row level crop evaluation zone for each crop row in thefield, each row level crop evaluation zone defining a window around thegeographic location of where a corresponding crop row was planted in thefield.Example 13 is the method of any or all previous examples whereinperforming spectral filtering comprises:filtering spectral information in the row level crop evaluation zones toidentify a crop density in the row level crop evaluation zones.Example 14 is the method of any or all previous examples and furthercomprising:dividing the image of the field into a grid with grid sections, eachgrid section corresponding to a geographic area in the field, whereingenerating an emergence metric includes generating an emergence metric,indicative of an emergence characteristic, corresponding to each gridsection in the grid.Example 15 is the method of any or all previous examples and furthercomprising:detecting a user request input; andgenerating a crop display that visually displays the grid sections andvisual indicia indicative of the corresponding emergence metrics.Example 16 is the method of any or all previous examples whereingenerating a crop display comprises:generating color coded grid sections as the visual indicia correspondingto the emergence metric for each grid section.Example 17 is the method of any or all previous examples whereingenerating a crop display comprises:generating alphanumeric information on each of the grid sections as thevisual indicia corresponding to the emergence metric for each gridsection.Example 18 is the method of any or all previous examples whereingenerating a crop display comprises:generating a user actuatable link, for each given grid section, the useractuatable link being actuated to navigate to a video image of thefield, corresponding to the given grid section.Example 19 is a computing system, comprising:spatial filtering logic that receives image data indicative of an imageof a field and crop location data indicative of a geographic location ofwhere crop rows were planted in the field, and that identifies a rowlevel crop evaluation zone for each crop row in the field, each rowlevel crop evaluation zone defining a window around the geographiclocation of where a corresponding crop row was planted in the field;spectral filtering logic that filters spectral information in the rowlevel crop evaluation zones to identify a crop density in the row levelcrop evaluation zones;grid generation logic that divides the image of the field into a gridwith grid sections, each grid section corresponding to a geographic areain the field;emergence metric generation logic that logic generates an emergencemetric, indicative of an emergence characteristic, corresponding to eachgrid section in the grid; anda user interface component that generates a crop display that visuallydisplays the grid sections and visual indicia indicative of thecorresponding emergence metrics.Example 20 is the computing system of any or all previous examples andfurther comprising:video link generation logic that generates a user actuatable link, foreach given grid section, the user interface component displaying theuser actuatable link for each given grid section on the given gridsection on the on the crop display, each link being actuatable tonavigate to a video image of a portion of the field, corresponding tothe given grid section.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A computing system, comprising: spatial filteringlogic that receives image data indicative of an image of a field andcrop location data indicative of a geographic location of crops in thefield and identifies a crop evaluation zone, on the image, based on thecrop location data; and emergence metric generation logic that generatesan emergence metric indicative of an emergence characteristic of thecrops in the crop evaluation zone in the field, based on spectral imagedata corresponding to the crop evaluation zone in the image.
 2. Thecomputing system of claim 1 wherein the crop location data identifies ageographic location of where crop rows were planted in the field andwherein the spatial filtering logic identifies a row level cropevaluation zone for each crop row in the field, each row level cropevaluation zone defining a window around the geographic location ofwhere a corresponding crop row was planted in the field.
 3. Thecomputing system of claim 2 and further comprising: spectral filteringlogic that filters spectral information in the row level crop evaluationzones to identify a crop density in the row level crop evaluation zones.4. The computing system of claim 3 wherein the spectral filtering logicconfines the filtering of the spectral information to the spectralinformation in the crop evaluation zones.
 5. The computing system ofclaim 3 and further comprising: grid generation logic that divides theimage of the field into a grid with grid sections, each grid sectioncorresponding to a geographic area in the field.
 6. The computing systemof claim 5 wherein the emergence metric generation logic generates anemergence metric, indicative of an emergence characteristic,corresponding to each grid section in the grid.
 7. The computing systemof claim 6 and further comprising: a runtime system that detects a userrequest input and generates a crop display that visually displays thegrid sections and visual indicia indicative of the correspondingemergence metrics.
 8. The computing system of claim 7 wherein theruntime system displays color coded grid sections as the visual indiciacorresponding to the emergence metric for each grid section.
 9. Thecomputing system of claim 7 wherein the runtime system displaysalphanumeric information on each of the grid sections as the visualindicia corresponding to the emergence metric for each grid section. 10.The computing system of claim 8 and further comprising: video linkgeneration logic that generates a user actuatable link, for each givengrid section, the user actuatable link linking to a video image of thefield, corresponding to the given grid section.
 11. A method,comprising: receiving a field image of a field; receiving crop locationdata indicative of a geographic location of crops in the field;identifying a crop evaluation zone, on the image, based on the croplocation data; performing spectral filtering on the crop evaluation zoneto identify crops in the crop evaluation zone; and generating anemergence metric indicative of an emergence characteristic of theidentified crops in the crop evaluation zone; and generating a userinterface indicative of the emergence metric visually corresponding tothe crop evaluation zone.
 12. The method of claim 11 wherein the croplocation data identifies a geographic location of where crop rows wereplanted in the field and wherein identifying a crop evaluation zonecomprises: identifying a row level crop evaluation zone for each croprow in the field, each row level crop evaluation zone defining a windowaround the geographic location of where a corresponding crop row wasplanted in the field.
 13. The method of claim 12 wherein performingspectral filtering comprises: filtering spectral information in the rowlevel crop evaluation zones to identify a crop density in the row levelcrop evaluation zones.
 14. The method of claim 13 and furthercomprising: dividing the image of the field into a grid with gridsections, each grid section corresponding to a geographic area in thefield, wherein generating an emergence metric includes generating anemergence metric, indicative of an emergence characteristic,corresponding to each grid section in the grid.
 15. The method of claim14 and further comprising: detecting a user request input; andgenerating a crop display that visually displays the grid sections andvisual indicia indicative of the corresponding emergence metrics. 16.The method of claim 15 wherein generating a crop display comprises:generating color coded grid sections as the visual indicia correspondingto the emergence metric for each grid section.
 17. The method of claim15 wherein generating a crop display comprises: generating alphanumericinformation on each of the grid sections as the visual indiciacorresponding to the emergence metric for each grid section.
 18. Themethod of claim 15 wherein generating a crop display comprises:generating a user actuatable link, for each given grid section, the useractuatable link being actuated to navigate to a video image of thefield, corresponding to the given grid section.
 19. A computing system,comprising: spatial filtering logic that receives image data indicativeof an image of a field and crop location data indicative of a geographiclocation of where crop rows were planted in the field, and thatidentifies a row level crop evaluation zone for each crop row in thefield, each row level crop evaluation zone defining a window around thegeographic location of where a corresponding crop row was planted in thefield; spectral filtering logic that filters spectral information in therow level crop evaluation zones to identify a crop density in the rowlevel crop evaluation zones; grid generation logic that divides theimage of the field into a grid with grid sections, each grid sectioncorresponding to a geographic area in the field; emergence metricgeneration logic that logic generates an emergence metric, indicative ofan emergence characteristic, corresponding to each grid section in thegrid; and a user interface component that generates a crop display thatvisually displays the grid sections and visual indicia indicative of thecorresponding emergence metrics
 20. The computing system of claim 19 andfurther comprising: video link generation logic that generates a useractuatable link, for each given grid section, the user interfacecomponent displaying the user actuatable link for each given gridsection on the given grid section on the on the crop display, each linkbeing actuatable to navigate to a video image of a portion of the field,corresponding to the given grid section.